{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MNGUB4GZITF5XYZJLFGW2MPT5Q","short_pith_number":"pith:MNGUB4GZ","schema_version":"1.0","canonical_sha256":"634d40f0d944cbdbe329594d6d31f3ec2a499d43e8844eb1d66530671ef79018","source":{"kind":"arxiv","id":"2505.06185","version":2},"attestation_state":"computed","paper":{"title":"Brain Hematoma Marker Recognition Using Multitask Learning: SwinTransformer and Swin-Unet","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Kodai Hirata, Tsuyoshi Okita","submitted_at":"2025-05-09T16:54:26Z","abstract_excerpt":"This paper proposes a method MTL-Swin-Unet which is multi-task learning using transformers for classification and semantic segmentation. For spurious-correlation problems, this method allows us to enhance the image representation with two other image representations: representation obtained by semantic segmentation and representation obtained by image reconstruction. In our experiments, the proposed method outperformed in F-value measure than other classifiers when the test data included slices from the same patient (no covariate shift). Similarly, when the test data did not include slices fro"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2505.06185","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-09T16:54:26Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"a6ac1611b644f35cf5ef28d2505f7485e942af00139f720440afe5fb003d692e","abstract_canon_sha256":"daeea3b22134ad7bce7ba14d962e6085d00b245e6bca277b236e9b68a84408f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:02:24.601325Z","signature_b64":"cAMwOoWcFdrmglC75+BnUDxYgdt/BM4W/wc8uS9ixwS/KD19MhDjki91WHVDD47SkcCd0vY/aK8vFa0VwAWnBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"634d40f0d944cbdbe329594d6d31f3ec2a499d43e8844eb1d66530671ef79018","last_reissued_at":"2026-07-05T11:02:24.600861Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:02:24.600861Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Brain Hematoma Marker Recognition Using Multitask Learning: SwinTransformer and Swin-Unet","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Kodai Hirata, Tsuyoshi Okita","submitted_at":"2025-05-09T16:54:26Z","abstract_excerpt":"This paper proposes a method MTL-Swin-Unet which is multi-task learning using transformers for classification and semantic segmentation. For spurious-correlation problems, this method allows us to enhance the image representation with two other image representations: representation obtained by semantic segmentation and representation obtained by image reconstruction. In our experiments, the proposed method outperformed in F-value measure than other classifiers when the test data included slices from the same patient (no covariate shift). Similarly, when the test data did not include slices fro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.06185","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2505.06185/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.06185","created_at":"2026-07-05T11:02:24.600921+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.06185v2","created_at":"2026-07-05T11:02:24.600921+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.06185","created_at":"2026-07-05T11:02:24.600921+00:00"},{"alias_kind":"pith_short_12","alias_value":"MNGUB4GZITF5","created_at":"2026-07-05T11:02:24.600921+00:00"},{"alias_kind":"pith_short_16","alias_value":"MNGUB4GZITF5XYZJ","created_at":"2026-07-05T11:02:24.600921+00:00"},{"alias_kind":"pith_short_8","alias_value":"MNGUB4GZ","created_at":"2026-07-05T11:02:24.600921+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MNGUB4GZITF5XYZJLFGW2MPT5Q","json":"https://pith.science/pith/MNGUB4GZITF5XYZJLFGW2MPT5Q.json","graph_json":"https://pith.science/api/pith-number/MNGUB4GZITF5XYZJLFGW2MPT5Q/graph.json","events_json":"https://pith.science/api/pith-number/MNGUB4GZITF5XYZJLFGW2MPT5Q/events.json","paper":"https://pith.science/paper/MNGUB4GZ"},"agent_actions":{"view_html":"https://pith.science/pith/MNGUB4GZITF5XYZJLFGW2MPT5Q","download_json":"https://pith.science/pith/MNGUB4GZITF5XYZJLFGW2MPT5Q.json","view_paper":"https://pith.science/paper/MNGUB4GZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.06185&json=true","fetch_graph":"https://pith.science/api/pith-number/MNGUB4GZITF5XYZJLFGW2MPT5Q/graph.json","fetch_events":"https://pith.science/api/pith-number/MNGUB4GZITF5XYZJLFGW2MPT5Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MNGUB4GZITF5XYZJLFGW2MPT5Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MNGUB4GZITF5XYZJLFGW2MPT5Q/action/storage_attestation","attest_author":"https://pith.science/pith/MNGUB4GZITF5XYZJLFGW2MPT5Q/action/author_attestation","sign_citation":"https://pith.science/pith/MNGUB4GZITF5XYZJLFGW2MPT5Q/action/citation_signature","submit_replication":"https://pith.science/pith/MNGUB4GZITF5XYZJLFGW2MPT5Q/action/replication_record"}},"created_at":"2026-07-05T11:02:24.600921+00:00","updated_at":"2026-07-05T11:02:24.600921+00:00"}